{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metrics in probabilistic forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In point estimate forecasting, the model outputs a single value that ideally represents the most likely value of the time series at future steps. In this scenario, the quality of the predictions can be assessed by comparing the predicted value with the true value of the series. Examples of metrics used for this purpose includes Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).\n",
    "\n",
    "In probabilistic forecasting, however, the model does not produce a single value, but rather a representation of the entire distribution of possible predicted values. In practice, this is often represented by a sample of the underlying distribution (e.g. 50 possible predicted values) or by specific quantiles that capture most of the information in the distribution. This approach provides richer insights by allowing the creation of prediction intervals - ranges within which the true value is likely to fall.\n",
    "\n",
    "In this context, the quality of the predictions cannot be assessed using the same metrics as in point estimate forecasting. Instead, we need to use metrics that are specifically designed to evaluate the quality of probabilistic forecasts.\n",
    "\n",
    "This notebook explores some metrics that can be used to assess the quality of probabilistic forecasts:\n",
    "\n",
    "+ Coverage: The proportion of the true values that fall within the prediction intervals.\n",
    "\n",
    "+ Interval width: The average width of the prediction intervals.\n",
    "\n",
    "+ Interval area: The area covered by the prediction intervals.\n",
    "\n",
    "+ [Continuous Ranked Probability Score (CRPS)](../faq/probabilistic-forecasting-crps-score.html): A scoring rule that measures the distance between the predicted cumulative distribution function and the true cumulative distribution function.\n",
    "\n",
    "In general, the goal is to produce prediction intervals that are as narrow as possible while still capturing the true values with the desired probability. This is a trade-off between the width of the intervals and the coverage of the true values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "<p>For more examples on how to use probabilistic forecasting, check out the following articles:</p>\n",
    "<ul>\n",
    "    <li>\n",
    "        <a href=\"https://cienciadedatos.net/documentos/py42-probabilistic-forecasting\" target=\"_blank\">\n",
    "            Probabilistic forecasting with machine learning\n",
    "        </a>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"https://cienciadedatos.net/documentos/py60-probabilistic-forecasting-prediction-intervals-multi-step-forecasting\" target=\"_blank\">\n",
    "            Probabilistic forecasting: prediction intervals for multi-step time series forecasting\n",
    "        </a>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"../faq/probabilistic-forecasting-crps-score.html\" target=\"_blank\">\n",
    "            Continuous Ranked Probability Score (CRPS) in probabilistic forecasting\n",
    "        </a>\n",
    "    </li>\n",
    "</ul>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data processing\n",
    "# ==============================================================================\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from skforecast.datasets import fetch_dataset\n",
    "\n",
    "# Plots\n",
    "# ==============================================================================\n",
    "import matplotlib.pyplot as plt\n",
    "from skforecast.plot import set_dark_theme\n",
    "from skforecast.plot import plot_prediction_intervals\n",
    "from skforecast.plot import plot_residuals\n",
    "\n",
    "# Modelling and Forecasting\n",
    "# ==============================================================================\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from feature_engine.datetime import DatetimeFeatures\n",
    "from feature_engine.creation import CyclicalFeatures\n",
    "from feature_engine.timeseries.forecasting import LagFeatures\n",
    "from feature_engine.timeseries.forecasting import WindowFeatures\n",
    "from skforecast.recursive import ForecasterRecursive\n",
    "from skforecast.model_selection import TimeSeriesFold\n",
    "from skforecast.model_selection import backtesting_forecaster\n",
    "from skforecast.preprocessing import RollingFeatures\n",
    "from skforecast.metrics import calculate_coverage, crps_from_quantiles\n",
    "\n",
    "# Warnings configuration\n",
    "# ==============================================================================\n",
    "import warnings\n",
    "warnings.filterwarnings('once')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭───────────────────────────────────── <span style=\"font-weight: bold\">ett_m2</span> ─────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Data from an electricity transformer station was collected between July 2016 and │\n",
       "│ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data    │\n",
       "│ points). Each data point consists of 8 features, including the date of the       │\n",
       "│ point, the predictive value \"Oil Temperature (OT)\", and 6 different types of     │\n",
       "│ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │\n",
       "│ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL),   │\n",
       "│ Low UseLess Load (LULL).                                                         │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Zhou, Haoyi &amp; Zhang, Shanghang &amp; Peng, Jieqi &amp; Zhang, Shuai &amp; Li, Jianxin &amp;      │\n",
       "│ Xiong, Hui &amp; Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for   │\n",
       "│ Long Sequence Time-Series Forecasting.                                           │\n",
       "│ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436).                   │\n",
       "│ https://github.com/zhouhaoyi/ETDataset                                           │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/ETTm2.csv                                                     │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 69680 rows x 7 columns                                                    │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭───────────────────────────────────── \u001b[1mett_m2\u001b[0m ─────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Data from an electricity transformer station was collected between July 2016 and │\n",
       "│ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data    │\n",
       "│ points). Each data point consists of 8 features, including the date of the       │\n",
       "│ point, the predictive value \"Oil Temperature (OT)\", and 6 different types of     │\n",
       "│ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │\n",
       "│ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL),   │\n",
       "│ Low UseLess Load (LULL).                                                         │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Zhou, Haoyi & Zhang, Shanghang & Peng, Jieqi & Zhang, Shuai & Li, Jianxin &      │\n",
       "│ Xiong, Hui & Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for   │\n",
       "│ Long Sequence Time-Series Forecasting.                                           │\n",
       "│ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436).                   │\n",
       "│ https://github.com/zhouhaoyi/ETDataset                                           │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/ETTm2.csv                                                     │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mShape:\u001b[0m 69680 rows x 7 columns                                                    │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HUFL</th>\n",
       "      <th>HULL</th>\n",
       "      <th>MUFL</th>\n",
       "      <th>MULL</th>\n",
       "      <th>LUFL</th>\n",
       "      <th>LULL</th>\n",
       "      <th>OT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-01 01:00:00</th>\n",
       "      <td>38.784501</td>\n",
       "      <td>10.88975</td>\n",
       "      <td>34.753500</td>\n",
       "      <td>8.55100</td>\n",
       "      <td>4.12575</td>\n",
       "      <td>1.26050</td>\n",
       "      <td>37.83825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01 02:00:00</th>\n",
       "      <td>36.041249</td>\n",
       "      <td>9.44475</td>\n",
       "      <td>32.696001</td>\n",
       "      <td>7.13700</td>\n",
       "      <td>3.59025</td>\n",
       "      <td>0.62900</td>\n",
       "      <td>36.84925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01 03:00:00</th>\n",
       "      <td>38.240000</td>\n",
       "      <td>11.41350</td>\n",
       "      <td>35.343501</td>\n",
       "      <td>9.10725</td>\n",
       "      <td>3.06000</td>\n",
       "      <td>0.31175</td>\n",
       "      <td>35.91575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          HUFL      HULL       MUFL     MULL     LUFL  \\\n",
       "date                                                                    \n",
       "2016-07-01 01:00:00  38.784501  10.88975  34.753500  8.55100  4.12575   \n",
       "2016-07-01 02:00:00  36.041249   9.44475  32.696001  7.13700  3.59025   \n",
       "2016-07-01 03:00:00  38.240000  11.41350  35.343501  9.10725  3.06000   \n",
       "\n",
       "                        LULL        OT  \n",
       "date                                    \n",
       "2016-07-01 01:00:00  1.26050  37.83825  \n",
       "2016-07-01 02:00:00  0.62900  36.84925  \n",
       "2016-07-01 03:00:00  0.31175  35.91575  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data\n",
    "# ==============================================================================\n",
    "data = fetch_dataset('ett_m2')\n",
    "data = data.resample(rule=\"1h\", closed=\"left\", label=\"right\").mean()\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dates train      : 2016-07-01 01:00:00 --- 2017-10-01 23:00:00  (n=10991)\n",
      "Dates validation : 2017-10-02 00:00:00 --- 2018-04-03 23:00:00  (n=4416)\n",
      "Dates test       : 2018-04-04 00:00:00 --- 2018-06-26 20:00:00  (n=2013)\n"
     ]
    }
   ],
   "source": [
    "# Split train-validation-test\n",
    "# ==============================================================================\n",
    "end_train = '2017-10-01 23:59:00'\n",
    "end_validation = '2018-04-03 23:59:00'\n",
    "data_train = data.loc[: end_train, :]\n",
    "data_val   = data.loc[end_train:end_validation, :]\n",
    "data_test  = data.loc[end_validation:, :]\n",
    "\n",
    "print(f\"Dates train      : {data_train.index.min()} --- {data_train.index.max()}  (n={len(data_train)})\")\n",
    "print(f\"Dates validation : {data_val.index.min()} --- {data_val.index.max()}  (n={len(data_val)})\")\n",
    "print(f\"Dates test       : {data_test.index.min()} --- {data_test.index.max()}  (n={len(data_test)})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot partitions of the target series\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "plt.rcParams['lines.linewidth'] = 0.5\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "ax.plot(data_train['OT'], label='Train')\n",
    "ax.plot(data_val['OT'], label='Validation')\n",
    "ax.plot(data_test['OT'], label='Test')\n",
    "ax.set_title('Oil Temperature')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot partitions after differencing\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "ax.plot(data_train['OT'].diff(1), label='Train')\n",
    "ax.plot(data_val['OT'].diff(1), label='Validation')\n",
    "ax.plot(data_test['OT'].diff(1), label='Test')\n",
    "ax.set_title('Differenced Oil Temperature')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;datetimefeatures&#x27;,\n",
       "                 DatetimeFeatures(drop_original=False,\n",
       "                                  features_to_extract=[&#x27;year&#x27;, &#x27;month&#x27;, &#x27;week&#x27;,\n",
       "                                                       &#x27;day_of_week&#x27;, &#x27;hour&#x27;],\n",
       "                                  variables=&#x27;index&#x27;)),\n",
       "                (&#x27;lagfeatures&#x27;,\n",
       "                 LagFeatures(periods=[1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14,\n",
       "                                      15, 16, 17, 18, 19, 20, 21, 23, 24, 42],\n",
       "                             variables=[&#x27;HUFL&#x27;, &#x27;MUFL&#x27;, &#x27;MULL&#x27;, &#x27;HULL&#x27;, &#x27;LUFL&#x27;,\n",
       "                                        &#x27;LULL&#x27;])),\n",
       "                (&#x27;windowfeatures&#x27;,\n",
       "                 WindowFeatures(freq=&#x27;1h&#x27;, functions=[&#x27;mean&#x27;, &#x27;max&#x27;, &#x27;min&#x27;],\n",
       "                                variables=[&#x27;HUFL&#x27;, &#x27;MUFL&#x27;, &#x27;MULL&#x27;, &#x27;HULL&#x27;,\n",
       "                                           &#x27;LUFL&#x27;, &#x27;LULL&#x27;],\n",
       "                                window=[&#x27;1D&#x27;, &#x27;7D&#x27;])),\n",
       "                (&#x27;cyclicalfeatures&#x27;,\n",
       "                 CyclicalFeatures(drop_original=True,\n",
       "                                  max_values={&#x27;day_of_week&#x27;: 7, &#x27;hour&#x27;: 24,\n",
       "                                              &#x27;month&#x27;: 12, &#x27;week&#x27;: 52},\n",
       "                                  variables=[&#x27;month&#x27;, &#x27;week&#x27;, &#x27;day_of_week&#x27;,\n",
       "                                             &#x27;hour&#x27;]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;datetimefeatures&#x27;,\n",
       "                 DatetimeFeatures(drop_original=False,\n",
       "                                  features_to_extract=[&#x27;year&#x27;, &#x27;month&#x27;, &#x27;week&#x27;,\n",
       "                                                       &#x27;day_of_week&#x27;, &#x27;hour&#x27;],\n",
       "                                  variables=&#x27;index&#x27;)),\n",
       "                (&#x27;lagfeatures&#x27;,\n",
       "                 LagFeatures(periods=[1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14,\n",
       "                                      15, 16, 17, 18, 19, 20, 21, 23, 24, 42],\n",
       "                             variables=[&#x27;HUFL&#x27;, &#x27;MUFL&#x27;, &#x27;MULL&#x27;, &#x27;HULL&#x27;, &#x27;LUFL&#x27;,\n",
       "                                        &#x27;LULL&#x27;])),\n",
       "                (&#x27;windowfeatures&#x27;,\n",
       "                 WindowFeatures(freq=&#x27;1h&#x27;, functions=[&#x27;mean&#x27;, &#x27;max&#x27;, &#x27;min&#x27;],\n",
       "                                variables=[&#x27;HUFL&#x27;, &#x27;MUFL&#x27;, &#x27;MULL&#x27;, &#x27;HULL&#x27;,\n",
       "                                           &#x27;LUFL&#x27;, &#x27;LULL&#x27;],\n",
       "                                window=[&#x27;1D&#x27;, &#x27;7D&#x27;])),\n",
       "                (&#x27;cyclicalfeatures&#x27;,\n",
       "                 CyclicalFeatures(drop_original=True,\n",
       "                                  max_values={&#x27;day_of_week&#x27;: 7, &#x27;hour&#x27;: 24,\n",
       "                                              &#x27;month&#x27;: 12, &#x27;week&#x27;: 52},\n",
       "                                  variables=[&#x27;month&#x27;, &#x27;week&#x27;, &#x27;day_of_week&#x27;,\n",
       "                                             &#x27;hour&#x27;]))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>DatetimeFeatures</div></div></label><div class=\"sk-toggleable__content \"><pre>DatetimeFeatures(drop_original=False,\n",
       "                 features_to_extract=[&#x27;year&#x27;, &#x27;month&#x27;, &#x27;week&#x27;, &#x27;day_of_week&#x27;,\n",
       "                                      &#x27;hour&#x27;],\n",
       "                 variables=&#x27;index&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>LagFeatures</div></div></label><div class=\"sk-toggleable__content \"><pre>LagFeatures(periods=[1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "                     19, 20, 21, 23, 24, 42],\n",
       "            variables=[&#x27;HUFL&#x27;, &#x27;MUFL&#x27;, &#x27;MULL&#x27;, &#x27;HULL&#x27;, &#x27;LUFL&#x27;, &#x27;LULL&#x27;])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>WindowFeatures</div></div></label><div class=\"sk-toggleable__content \"><pre>WindowFeatures(freq=&#x27;1h&#x27;, functions=[&#x27;mean&#x27;, &#x27;max&#x27;, &#x27;min&#x27;],\n",
       "               variables=[&#x27;HUFL&#x27;, &#x27;MUFL&#x27;, &#x27;MULL&#x27;, &#x27;HULL&#x27;, &#x27;LUFL&#x27;, &#x27;LULL&#x27;],\n",
       "               window=[&#x27;1D&#x27;, &#x27;7D&#x27;])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>CyclicalFeatures</div></div></label><div class=\"sk-toggleable__content \"><pre>CyclicalFeatures(drop_original=True,\n",
       "                 max_values={&#x27;day_of_week&#x27;: 7, &#x27;hour&#x27;: 24, &#x27;month&#x27;: 12,\n",
       "                             &#x27;week&#x27;: 52},\n",
       "                 variables=[&#x27;month&#x27;, &#x27;week&#x27;, &#x27;day_of_week&#x27;, &#x27;hour&#x27;])</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('datetimefeatures',\n",
       "                 DatetimeFeatures(drop_original=False,\n",
       "                                  features_to_extract=['year', 'month', 'week',\n",
       "                                                       'day_of_week', 'hour'],\n",
       "                                  variables='index')),\n",
       "                ('lagfeatures',\n",
       "                 LagFeatures(periods=[1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14,\n",
       "                                      15, 16, 17, 18, 19, 20, 21, 23, 24, 42],\n",
       "                             variables=['HUFL', 'MUFL', 'MULL', 'HULL', 'LUFL',\n",
       "                                        'LULL'])),\n",
       "                ('windowfeatures',\n",
       "                 WindowFeatures(freq='1h', functions=['mean', 'max', 'min'],\n",
       "                                variables=['HUFL', 'MUFL', 'MULL', 'HULL',\n",
       "                                           'LUFL', 'LULL'],\n",
       "                                window=['1D', '7D'])),\n",
       "                ('cyclicalfeatures',\n",
       "                 CyclicalFeatures(drop_original=True,\n",
       "                                  max_values={'day_of_week': 7, 'hour': 24,\n",
       "                                              'month': 12, 'week': 52},\n",
       "                                  variables=['month', 'week', 'day_of_week',\n",
       "                                             'hour']))])"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HUFL</th>\n",
       "      <th>HULL</th>\n",
       "      <th>MUFL</th>\n",
       "      <th>MULL</th>\n",
       "      <th>LUFL</th>\n",
       "      <th>LULL</th>\n",
       "      <th>OT</th>\n",
       "      <th>year</th>\n",
       "      <th>HUFL_lag_1</th>\n",
       "      <th>MUFL_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>LULL_window_7D_max</th>\n",
       "      <th>LULL_window_7D_min</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>month_cos</th>\n",
       "      <th>week_sin</th>\n",
       "      <th>week_cos</th>\n",
       "      <th>day_of_week_sin</th>\n",
       "      <th>day_of_week_cos</th>\n",
       "      <th>hour_sin</th>\n",
       "      <th>hour_cos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-02 19:00:00</th>\n",
       "      <td>33.6330</td>\n",
       "      <td>9.08900</td>\n",
       "      <td>29.874751</td>\n",
       "      <td>6.95600</td>\n",
       "      <td>3.753</td>\n",
       "      <td>0.61025</td>\n",
       "      <td>28.719500</td>\n",
       "      <td>2016</td>\n",
       "      <td>32.5440</td>\n",
       "      <td>29.017001</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>0.258819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-02 20:00:00</th>\n",
       "      <td>31.7065</td>\n",
       "      <td>7.72750</td>\n",
       "      <td>27.884500</td>\n",
       "      <td>5.63600</td>\n",
       "      <td>3.753</td>\n",
       "      <td>0.67425</td>\n",
       "      <td>29.103875</td>\n",
       "      <td>2016</td>\n",
       "      <td>33.6330</td>\n",
       "      <td>29.874751</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-02 21:00:00</th>\n",
       "      <td>31.8110</td>\n",
       "      <td>7.81125</td>\n",
       "      <td>27.362000</td>\n",
       "      <td>5.18025</td>\n",
       "      <td>4.435</td>\n",
       "      <td>1.42075</td>\n",
       "      <td>29.598500</td>\n",
       "      <td>2016</td>\n",
       "      <td>31.7065</td>\n",
       "      <td>27.884500</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>0.707107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 184 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        HUFL     HULL       MUFL     MULL   LUFL     LULL  \\\n",
       "date                                                                        \n",
       "2016-07-02 19:00:00  33.6330  9.08900  29.874751  6.95600  3.753  0.61025   \n",
       "2016-07-02 20:00:00  31.7065  7.72750  27.884500  5.63600  3.753  0.67425   \n",
       "2016-07-02 21:00:00  31.8110  7.81125  27.362000  5.18025  4.435  1.42075   \n",
       "\n",
       "                            OT  year  HUFL_lag_1  MUFL_lag_1  ...  \\\n",
       "date                                                          ...   \n",
       "2016-07-02 19:00:00  28.719500  2016     32.5440   29.017001  ...   \n",
       "2016-07-02 20:00:00  29.103875  2016     33.6330   29.874751  ...   \n",
       "2016-07-02 21:00:00  29.598500  2016     31.7065   27.884500  ...   \n",
       "\n",
       "                     LULL_window_7D_max  LULL_window_7D_min  month_sin  \\\n",
       "date                                                                     \n",
       "2016-07-02 19:00:00              1.2605                 0.0       -0.5   \n",
       "2016-07-02 20:00:00              1.2605                 0.0       -0.5   \n",
       "2016-07-02 21:00:00              1.2605                 0.0       -0.5   \n",
       "\n",
       "                     month_cos      week_sin  week_cos  day_of_week_sin  \\\n",
       "date                                                                      \n",
       "2016-07-02 19:00:00  -0.866025  1.224647e-16      -1.0        -0.974928   \n",
       "2016-07-02 20:00:00  -0.866025  1.224647e-16      -1.0        -0.974928   \n",
       "2016-07-02 21:00:00  -0.866025  1.224647e-16      -1.0        -0.974928   \n",
       "\n",
       "                     day_of_week_cos  hour_sin  hour_cos  \n",
       "date                                                      \n",
       "2016-07-02 19:00:00        -0.222521 -0.965926  0.258819  \n",
       "2016-07-02 20:00:00        -0.222521 -0.866025  0.500000  \n",
       "2016-07-02 21:00:00        -0.222521 -0.707107  0.707107  \n",
       "\n",
       "[3 rows x 184 columns]"
      ]
     },
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    }
   ],
   "source": [
    "# Calendar features\n",
    "# ==============================================================================\n",
    "features_to_extract = [\n",
    "    'year',\n",
    "    'month',\n",
    "    'week',\n",
    "    'day_of_week',\n",
    "    'hour'\n",
    "]\n",
    "calendar_transformer = DatetimeFeatures(\n",
    "    variables           = 'index',\n",
    "    features_to_extract = features_to_extract,\n",
    "    drop_original       = False,\n",
    ")\n",
    "\n",
    "# Lags of exogenous variables\n",
    "# ==============================================================================\n",
    "lag_transformer = LagFeatures(\n",
    "    variables = [\"HUFL\", \"MUFL\", \"MULL\", \"HULL\", \"LUFL\", \"LULL\"],\n",
    "    periods   = [1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 42]\n",
    ")\n",
    "\n",
    "# Rolling features for exogenous variables\n",
    "# ==============================================================================\n",
    "wf_transformer = WindowFeatures(\n",
    "    variables = [\"HUFL\", \"MUFL\", \"MULL\", \"HULL\", \"LUFL\", \"LULL\"],\n",
    "    window    = [\"1D\", \"7D\"],\n",
    "    functions = [\"mean\", \"max\", \"min\"],\n",
    "    freq      = \"1h\",\n",
    ")\n",
    "\n",
    "# Cyclical encoding of calendar features\n",
    "# ==============================================================================\n",
    "features_to_encode = [\n",
    "    \"month\",\n",
    "    \"week\",\n",
    "    \"day_of_week\",\n",
    "    \"hour\",\n",
    "]\n",
    "max_values = {\n",
    "    \"month\": 12,\n",
    "    \"week\": 52,\n",
    "    \"day_of_week\": 7,\n",
    "    \"hour\": 24,\n",
    "}\n",
    "cyclical_encoder = CyclicalFeatures(\n",
    "                       variables     = features_to_encode,\n",
    "                       max_values    = max_values,\n",
    "                       drop_original = True\n",
    "                   )\n",
    "\n",
    "exog_transformer = make_pipeline(\n",
    "                       calendar_transformer,\n",
    "                       lag_transformer,\n",
    "                       wf_transformer,\n",
    "                       cyclical_encoder\n",
    "                   )\n",
    "display(exog_transformer)\n",
    "\n",
    "data = exog_transformer.fit_transform(data)\n",
    "# Remove rows with NaNs created by lag features\n",
    "data = data.dropna()\n",
    "display(data.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exogenous features\n",
    "# ==============================================================================\n",
    "lags = [1, 2, 3, 4, 5, 6, 9, 12, 15, 17, 20, 23, 24, 42]\n",
    "exog_features = [\n",
    "    'HUFL', 'HUFL_lag_1', 'HUFL_lag_12', 'HUFL_lag_13', 'HUFL_lag_15', 'HUFL_lag_19',\n",
    "    'HUFL_lag_2', 'HUFL_lag_20', 'HUFL_lag_23', 'HUFL_lag_4', 'HUFL_lag_5', 'HUFL_lag_9',\n",
    "    'HUFL_window_1D_mean', 'HULL', 'HULL_lag_1', 'HULL_lag_12', 'HULL_lag_14',\n",
    "    'HULL_lag_15', 'HULL_lag_2', 'HULL_lag_20', 'HULL_lag_21', 'HULL_lag_23',\n",
    "    'HULL_lag_3', 'HULL_lag_4', 'HULL_window_1D_mean', 'LUFL', 'LUFL_lag_1',\n",
    "    'LUFL_lag_10', 'LUFL_lag_15', 'LUFL_lag_19', 'LUFL_lag_2', 'LUFL_lag_20',\n",
    "    'LUFL_lag_23', 'LUFL_lag_3', 'LUFL_lag_4', 'LUFL_lag_5', 'LUFL_window_1D_mean',\n",
    "    'LULL', 'LULL_lag_1', 'LULL_lag_12', 'LULL_lag_13', 'LULL_lag_14', 'LULL_lag_18',\n",
    "    'LULL_lag_19', 'LULL_lag_20', 'LULL_lag_21', 'LULL_lag_23', 'LULL_lag_24',\n",
    "    'LULL_lag_4', 'LULL_lag_5', 'LULL_lag_6', 'LULL_window_1D_max', 'LULL_window_1D_min',\n",
    "    'MUFL', 'MUFL_lag_1', 'MUFL_lag_11', 'MUFL_lag_12', 'MUFL_lag_13', 'MUFL_lag_15',\n",
    "    'MUFL_lag_2', 'MUFL_lag_20', 'MUFL_lag_23', 'MUFL_lag_4', 'MUFL_lag_9',\n",
    "    'MUFL_window_1D_mean', 'MULL', 'MULL_lag_1', 'MULL_lag_11', 'MULL_lag_12',\n",
    "    'MULL_lag_13', 'MULL_lag_14', 'MULL_lag_15', 'MULL_lag_17', 'MULL_lag_19',\n",
    "    'MULL_lag_2', 'MULL_lag_20', 'MULL_lag_3', 'MULL_lag_4', 'MULL_lag_5', 'MULL_lag_9',\n",
    "    'MULL_window_1D_mean', 'MULL_window_1D_min', 'MULL_window_7D_mean', 'day_of_week_sin',\n",
    "    'hour_cos', 'hour_sin', 'month_cos', 'month_sin', 'week_cos', 'week_sin', 'year'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,184,212,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00b8d4; border-color: #00b8d4; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00b8d4;\"></i>\n",
    "    <b style=\"color: #00b8d4;\">&#9998 Note</b>\n",
    "</p>\n",
    "\n",
    "The lags, features and hyperparameters used in this document were selected after a hyperparameter optimization and feature selection process. For a more detailed visit the full document <a href=\"https://cienciadedatos.net/documentos/py60-probabilistic-forecasting-prediction-intervals-multi-step-forecasting\">Probabilistic forecasting: prediction intervals for multi-step time series forecasting</a>.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probabilistic forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create forecaster\n",
    "# ==============================================================================\n",
    "window_features = RollingFeatures(stats=['mean', 'min', 'max'], window_sizes=24)\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = Ridge(random_state=15926, alpha=1.1075),\n",
    "                 lags            = lags,\n",
    "                 window_features = window_features,\n",
    "                 differentiation = 1,\n",
    "                 binner_kwargs   = {'n_bins': 10}\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ea5613c0c1043bab3bd51e733f43805",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/184 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_absolute_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.382001</td>\n",
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       "   mean_absolute_error\n",
       "0             2.382001"
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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Backtesting on validation data to obtain out-sample residuals\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "         initial_train_size = len(data.loc[:end_train, :]),\n",
    "         steps              = 24,  # all hours of next day\n",
    "         differentiation    = 1,\n",
    "     )\n",
    "\n",
    "metric_val, predictions_val = backtesting_forecaster(\n",
    "                                  forecaster = forecaster,\n",
    "                                  y          = data.loc[:end_validation, 'OT'],\n",
    "                                  exog       = data.loc[:end_validation, exog_features],\n",
    "                                  cv         = cv,\n",
    "                                  metric     = 'mean_absolute_error'\n",
    "                              )\n",
    "\n",
    "display(metric_val)\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "data.loc[end_train:end_validation, 'OT'].plot(ax=ax, label='real value')\n",
    "predictions_val['pred'].plot(ax=ax, label='prediction')\n",
    "ax.set_title(\"Backtesting on validation data\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive    2462\n",
      "negative    1954\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Out-sample residuals distribution\n",
    "# ==============================================================================\n",
    "residuals = data.loc[predictions_val.index, 'OT'] - predictions_val['pred']\n",
    "print(pd.Series(np.where(residuals < 0, 'negative', 'positive')).value_counts())\n",
    "plt.rcParams.update({'font.size': 8})\n",
    "_ = plot_residuals(residuals=residuals, figsize=(7, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store out-sample residuals in the forecaster\n",
    "# ==============================================================================\n",
    "forecaster.fit(y=data.loc[:end_train, 'OT'], exog=data.loc[:end_train, exog_features])\n",
    "forecaster.set_out_sample_residuals(\n",
    "    y_true = data.loc[predictions_val.index, 'OT'], \n",
    "    y_pred = predictions_val['pred']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb3693b3f4e149c5a83307adea790a25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/84 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_absolute_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.872448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_absolute_error\n",
       "0             2.872448"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-04 00:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>32.748221</td>\n",
       "      <td>32.293918</td>\n",
       "      <td>33.421547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 01:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>31.947644</td>\n",
       "      <td>31.428145</td>\n",
       "      <td>33.195005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 02:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>31.052970</td>\n",
       "      <td>30.153244</td>\n",
       "      <td>32.860915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 03:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>30.109002</td>\n",
       "      <td>28.482297</td>\n",
       "      <td>32.571251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 04:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>29.229875</td>\n",
       "      <td>27.802629</td>\n",
       "      <td>32.305870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fold       pred  lower_bound  upper_bound\n",
       "2018-04-04 00:00:00     0  32.748221    32.293918    33.421547\n",
       "2018-04-04 01:00:00     0  31.947644    31.428145    33.195005\n",
       "2018-04-04 02:00:00     0  31.052970    30.153244    32.860915\n",
       "2018-04-04 03:00:00     0  30.109002    28.482297    32.571251\n",
       "2018-04-04 04:00:00     0  29.229875    27.802629    32.305870"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtesting with prediction intervals in test data using out-sample residuals\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "         initial_train_size = len(data.loc[:end_validation, :]),\n",
    "         steps              = 24,  # all hours of next day\n",
    "         differentiation    = 1\n",
    "     )\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster              = forecaster,\n",
    "                          y                       = data['OT'],\n",
    "                          exog                    = data[exog_features],\n",
    "                          cv                      = cv,\n",
    "                          metric                  = 'mean_absolute_error',\n",
    "                          interval                = [10, 90],  # 80% prediction interval\n",
    "                          interval_method         = 'bootstrapping',\n",
    "                          n_boot                  = 150,\n",
    "                          use_in_sample_residuals = False,  # Use out-sample residuals\n",
    "                          use_binned_residuals    = True\n",
    "                      )\n",
    "display(metric)\n",
    "predictions.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot intervals\n",
    "# ==============================================================================\n",
    "plt.rcParams['lines.linewidth'] = 1\n",
    "fig, ax = plt.subplots(figsize=(9, 4))\n",
    "plot_prediction_intervals(\n",
    "    predictions     = predictions,\n",
    "    y_true          = data_test,\n",
    "    target_variable = \"OT\",\n",
    "    initial_x_zoom  = None,\n",
    "    title           = \"Prediction interval in test data\",\n",
    "    xaxis_title     = \"Date time\",\n",
    "    yaxis_title     = \"OT\",\n",
    "    ax              = ax\n",
    ")\n",
    "fill_between_obj = ax.collections[0]\n",
    "fill_between_obj.set_facecolor('white')\n",
    "fill_between_obj.set_alpha(0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted interval coverage: 80.87 %\n",
      "Area of the interval: 19873.9\n",
      "Mean width of the interval: 9.87\n"
     ]
    }
   ],
   "source": [
    "# Empirical interval coverage (on test data)\n",
    "# ==============================================================================\n",
    "coverage = calculate_coverage(\n",
    "               y_true      = data.loc[end_validation:, 'OT'],\n",
    "               lower_bound = predictions[\"lower_bound\"], \n",
    "               upper_bound = predictions[\"upper_bound\"]\n",
    "           )\n",
    "print(f\"Predicted interval coverage: {round(100 * coverage, 2)} %\")\n",
    "\n",
    "# Mean widht and ara of the interval\n",
    "# ==============================================================================\n",
    "area = (predictions[\"upper_bound\"] - predictions[\"lower_bound\"]).sum()\n",
    "mean_width = (predictions[\"upper_bound\"] - predictions[\"lower_bound\"]).mean()\n",
    "print(f\"Area of the interval: {round(area, 2)}\")\n",
    "print(f\"Mean width of the interval: {round(mean_width, 2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction of multiple intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `backtesting_forecaster` function not only allows to estimate a single prediction interval, but also to estimate multiple quantiles (percentiles) from which multiple prediction intervals can be constructed. This is useful to evaluate the quality of the prediction intervals for a range of probabilities.\n",
    "\n",
    "Several percentiles are predicted and from these, prediction intervals are estimated for different nominal coverage levels - 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 95% - and their actual coverage is assessed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "  0%|          | 0/84 [00:00<?, ?it/s]"
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "      <th>p_2.5</th>\n",
       "      <th>p_5</th>\n",
       "      <th>p_10</th>\n",
       "      <th>p_15</th>\n",
       "      <th>p_20</th>\n",
       "      <th>p_25</th>\n",
       "      <th>p_30</th>\n",
       "      <th>p_35</th>\n",
       "      <th>...</th>\n",
       "      <th>p_55</th>\n",
       "      <th>p_60</th>\n",
       "      <th>p_65</th>\n",
       "      <th>p_70</th>\n",
       "      <th>p_75</th>\n",
       "      <th>p_80</th>\n",
       "      <th>p_85</th>\n",
       "      <th>p_90</th>\n",
       "      <th>p_95</th>\n",
       "      <th>p_97.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-04 00:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>32.748221</td>\n",
       "      <td>31.977111</td>\n",
       "      <td>32.079720</td>\n",
       "      <td>32.293918</td>\n",
       "      <td>32.462264</td>\n",
       "      <td>32.535513</td>\n",
       "      <td>32.571749</td>\n",
       "      <td>32.652022</td>\n",
       "      <td>32.785265</td>\n",
       "      <td>...</td>\n",
       "      <td>33.025805</td>\n",
       "      <td>33.070953</td>\n",
       "      <td>33.132935</td>\n",
       "      <td>33.199885</td>\n",
       "      <td>33.255073</td>\n",
       "      <td>33.293658</td>\n",
       "      <td>33.330013</td>\n",
       "      <td>33.421547</td>\n",
       "      <td>33.546763</td>\n",
       "      <td>33.805721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 01:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>31.947644</td>\n",
       "      <td>30.611061</td>\n",
       "      <td>31.185507</td>\n",
       "      <td>31.428145</td>\n",
       "      <td>31.587988</td>\n",
       "      <td>31.717603</td>\n",
       "      <td>31.866081</td>\n",
       "      <td>31.984188</td>\n",
       "      <td>32.070315</td>\n",
       "      <td>...</td>\n",
       "      <td>32.519054</td>\n",
       "      <td>32.659853</td>\n",
       "      <td>32.791033</td>\n",
       "      <td>32.868997</td>\n",
       "      <td>32.921711</td>\n",
       "      <td>32.993290</td>\n",
       "      <td>33.073593</td>\n",
       "      <td>33.195005</td>\n",
       "      <td>33.308497</td>\n",
       "      <td>33.484556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 02:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>31.052970</td>\n",
       "      <td>28.613107</td>\n",
       "      <td>29.604880</td>\n",
       "      <td>30.153244</td>\n",
       "      <td>30.325608</td>\n",
       "      <td>30.673197</td>\n",
       "      <td>30.913207</td>\n",
       "      <td>31.189854</td>\n",
       "      <td>31.402368</td>\n",
       "      <td>...</td>\n",
       "      <td>32.020048</td>\n",
       "      <td>32.096509</td>\n",
       "      <td>32.183147</td>\n",
       "      <td>32.299333</td>\n",
       "      <td>32.451905</td>\n",
       "      <td>32.588603</td>\n",
       "      <td>32.656948</td>\n",
       "      <td>32.860915</td>\n",
       "      <td>33.158009</td>\n",
       "      <td>33.299128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 03:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>30.109002</td>\n",
       "      <td>27.524392</td>\n",
       "      <td>28.040126</td>\n",
       "      <td>28.482297</td>\n",
       "      <td>29.131206</td>\n",
       "      <td>29.614509</td>\n",
       "      <td>29.989295</td>\n",
       "      <td>30.330817</td>\n",
       "      <td>30.542676</td>\n",
       "      <td>...</td>\n",
       "      <td>31.413216</td>\n",
       "      <td>31.624016</td>\n",
       "      <td>31.760533</td>\n",
       "      <td>31.880495</td>\n",
       "      <td>31.930950</td>\n",
       "      <td>32.091862</td>\n",
       "      <td>32.389500</td>\n",
       "      <td>32.571251</td>\n",
       "      <td>32.965276</td>\n",
       "      <td>33.314962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 04:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>29.229875</td>\n",
       "      <td>26.708860</td>\n",
       "      <td>27.127498</td>\n",
       "      <td>27.802629</td>\n",
       "      <td>28.255452</td>\n",
       "      <td>28.937064</td>\n",
       "      <td>29.265236</td>\n",
       "      <td>29.381942</td>\n",
       "      <td>29.786477</td>\n",
       "      <td>...</td>\n",
       "      <td>30.858493</td>\n",
       "      <td>30.988576</td>\n",
       "      <td>31.179325</td>\n",
       "      <td>31.407085</td>\n",
       "      <td>31.570523</td>\n",
       "      <td>31.820700</td>\n",
       "      <td>31.959443</td>\n",
       "      <td>32.305870</td>\n",
       "      <td>32.768797</td>\n",
       "      <td>33.306518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fold       pred      p_2.5        p_5       p_10  \\\n",
       "2018-04-04 00:00:00     0  32.748221  31.977111  32.079720  32.293918   \n",
       "2018-04-04 01:00:00     0  31.947644  30.611061  31.185507  31.428145   \n",
       "2018-04-04 02:00:00     0  31.052970  28.613107  29.604880  30.153244   \n",
       "2018-04-04 03:00:00     0  30.109002  27.524392  28.040126  28.482297   \n",
       "2018-04-04 04:00:00     0  29.229875  26.708860  27.127498  27.802629   \n",
       "\n",
       "                          p_15       p_20       p_25       p_30       p_35  \\\n",
       "2018-04-04 00:00:00  32.462264  32.535513  32.571749  32.652022  32.785265   \n",
       "2018-04-04 01:00:00  31.587988  31.717603  31.866081  31.984188  32.070315   \n",
       "2018-04-04 02:00:00  30.325608  30.673197  30.913207  31.189854  31.402368   \n",
       "2018-04-04 03:00:00  29.131206  29.614509  29.989295  30.330817  30.542676   \n",
       "2018-04-04 04:00:00  28.255452  28.937064  29.265236  29.381942  29.786477   \n",
       "\n",
       "                     ...       p_55       p_60       p_65       p_70  \\\n",
       "2018-04-04 00:00:00  ...  33.025805  33.070953  33.132935  33.199885   \n",
       "2018-04-04 01:00:00  ...  32.519054  32.659853  32.791033  32.868997   \n",
       "2018-04-04 02:00:00  ...  32.020048  32.096509  32.183147  32.299333   \n",
       "2018-04-04 03:00:00  ...  31.413216  31.624016  31.760533  31.880495   \n",
       "2018-04-04 04:00:00  ...  30.858493  30.988576  31.179325  31.407085   \n",
       "\n",
       "                          p_75       p_80       p_85       p_90       p_95  \\\n",
       "2018-04-04 00:00:00  33.255073  33.293658  33.330013  33.421547  33.546763   \n",
       "2018-04-04 01:00:00  32.921711  32.993290  33.073593  33.195005  33.308497   \n",
       "2018-04-04 02:00:00  32.451905  32.588603  32.656948  32.860915  33.158009   \n",
       "2018-04-04 03:00:00  31.930950  32.091862  32.389500  32.571251  32.965276   \n",
       "2018-04-04 04:00:00  31.570523  31.820700  31.959443  32.305870  32.768797   \n",
       "\n",
       "                        p_97.5  \n",
       "2018-04-04 00:00:00  33.805721  \n",
       "2018-04-04 01:00:00  33.484556  \n",
       "2018-04-04 02:00:00  33.299128  \n",
       "2018-04-04 03:00:00  33.314962  \n",
       "2018-04-04 04:00:00  33.306518  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction intervals for different nominal coverages\n",
    "# ==============================================================================\n",
    "quantiles = [2.5, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 97.5]\n",
    "intervals = [[2.5, 97.5], [5, 95], [10, 90], [15, 85], [20, 80], [30, 70], [35, 65], [40, 60], [45, 55]]\n",
    "observed_coverages = []\n",
    "observed_areas = []\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster              = forecaster,\n",
    "                          y                       = data['OT'],\n",
    "                          exog                    = data[exog_features],\n",
    "                          cv                      = cv,\n",
    "                          metric                  = 'mean_absolute_error',\n",
    "                          interval                = quantiles,\n",
    "                          interval_method         = 'bootstrapping',\n",
    "                          n_boot                  = 150,\n",
    "                          use_in_sample_residuals = False,  # Use out-sample residuals\n",
    "                          use_binned_residuals    = True\n",
    "                      )\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Interval</th>\n",
       "      <th>Nominal coverage</th>\n",
       "      <th>Observed coverage</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[2.5, 97.5]</td>\n",
       "      <td>95.0</td>\n",
       "      <td>93.6</td>\n",
       "      <td>32010.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[5, 95]</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.1</td>\n",
       "      <td>26152.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[10, 90]</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.9</td>\n",
       "      <td>19873.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[15, 85]</td>\n",
       "      <td>70.0</td>\n",
       "      <td>71.7</td>\n",
       "      <td>15862.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[20, 80]</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.7</td>\n",
       "      <td>12775.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[30, 70]</td>\n",
       "      <td>40.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>7826.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[35, 65]</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>5708.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[40, 60]</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.9</td>\n",
       "      <td>3726.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[45, 55]</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1834.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Interval  Nominal coverage  Observed coverage     Area\n",
       "0  [2.5, 97.5]              95.0               93.6  32010.8\n",
       "1      [5, 95]              90.0               90.1  26152.9\n",
       "2     [10, 90]              80.0               80.9  19873.9\n",
       "3     [15, 85]              70.0               71.7  15862.6\n",
       "4     [20, 80]              60.0               63.7  12775.7\n",
       "5     [30, 70]              40.0               44.0   7826.7\n",
       "6     [35, 65]              30.0               32.9   5708.6\n",
       "7     [40, 60]              20.0               22.9   3726.1\n",
       "8     [45, 55]              10.0               11.1   1834.2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate coverage and area for each interval\n",
    "# ==============================================================================\n",
    "for interval in intervals:\n",
    "    observed_coverage = calculate_coverage(\n",
    "        y_true      = data.loc[end_validation:, 'OT'],\n",
    "        lower_bound = predictions[f\"p_{interval[0]}\"], \n",
    "        upper_bound = predictions[f\"p_{interval[1]}\"]\n",
    "    )\n",
    "    observed_area = (predictions[f\"p_{interval[1]}\"] - predictions[f\"p_{interval[0]}\"]).sum()\n",
    "    observed_coverages.append(100 * observed_coverage)\n",
    "    observed_areas.append(observed_area)\n",
    "\n",
    "results = pd.DataFrame({\n",
    "              'Interval': intervals,\n",
    "              'Nominal coverage': [interval[1] - interval[0] for interval in intervals],\n",
    "              'Observed coverage': observed_coverages,\n",
    "              'Area': observed_areas\n",
    "          })\n",
    "results.round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the [Continuous Ranked Probability Score (CRPS)](../faq/probabilistic-forecasting-crps-score.html) is calculated for each of the prediction using the function `crps_from_quantiles` and averaged to obtain a single value that summarizes the quality of the prediction intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CRPS: 62518.41\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OT</th>\n",
       "      <th>predicted_q</th>\n",
       "      <th>crps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-04 00:00:00</th>\n",
       "      <td>32.674500</td>\n",
       "      <td>[31.977110647741007, 32.079720381565885, 32.29...</td>\n",
       "      <td>6276.813891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 01:00:00</th>\n",
       "      <td>31.575750</td>\n",
       "      <td>[30.611061023950626, 31.18550716728485, 31.428...</td>\n",
       "      <td>7141.667190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 02:00:00</th>\n",
       "      <td>29.763125</td>\n",
       "      <td>[28.613107419216398, 29.604880406597363, 30.15...</td>\n",
       "      <td>10335.665443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            OT  \\\n",
       "2018-04-04 00:00:00  32.674500   \n",
       "2018-04-04 01:00:00  31.575750   \n",
       "2018-04-04 02:00:00  29.763125   \n",
       "\n",
       "                                                           predicted_q  \\\n",
       "2018-04-04 00:00:00  [31.977110647741007, 32.079720381565885, 32.29...   \n",
       "2018-04-04 01:00:00  [30.611061023950626, 31.18550716728485, 31.428...   \n",
       "2018-04-04 02:00:00  [28.613107419216398, 29.604880406597363, 30.15...   \n",
       "\n",
       "                             crps  \n",
       "2018-04-04 00:00:00   6276.813891  \n",
       "2018-04-04 01:00:00   7141.667190  \n",
       "2018-04-04 02:00:00  10335.665443  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average CRPS\n",
    "# ==============================================================================\n",
    "\n",
    "# Collapse predictions to a single column (two first are excluded)\n",
    "predicted_q = predictions.iloc[:, 2:].apply(lambda row: np.array(row), axis=1).to_frame(name='predicted_q')\n",
    "predicted_q = pd.concat([data.loc[end_validation:, 'OT'], predicted_q], axis=1)\n",
    "\n",
    "# Calculate CRPS for each row\n",
    "predicted_q['crps'] = predicted_q.apply(\n",
    "    lambda row: crps_from_quantiles(y_true=row['OT'], pred_quantiles=row['predicted_q'], quantile_levels=np.array(quantiles)), axis=1\n",
    ")\n",
    "\n",
    "crps = predicted_q['crps'].mean()\n",
    "print(f\"Average CRPS: {round(crps, 2)}\")\n",
    "predicted_q.head(3)"
   ]
  }
 ],
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